Information Systems and Data Management research strengths

Data Science and Information Systems researchers at UQ are tackling the challenges of big data, real-time analytics, data modelling and smart information use. The cutting-edge solutions developed at UQ will lead to user empowerment at an individual, corporate and societal level. Our researchers are making a sustained and influential contribution to the management, modelling, governance, integration, analysis and use of very large quantities of diverse and complex data in an interconnected world.

The quality of our research effort has been recognised nationally and internationally. The 2012 Excellence in Research for Australia exercise rated Information Systems research at UQ at the highest level, well above world standard. Our researchers have received numerous awards and fellowships, including Fellowships of the Australian Academy of Science and of the Australian Computer Society, Australian Research Council (ARC) Future Fellowships, and an Emerald Group Publishing Citation of Excellence Award.

UQ researchers collaborate with global industry leaders in the IT sector, renowned thought leaders, and a range of user organisations and communities. These collaborations span application areas such as intelligent transportation and logistics, water resource management, environmental studies, social computing, healthcare, compliance and risk management, IT governance, and business process management.

UQ recently headquartered a national research network on Enterprise Information Infrastructure (2005-2010) that profoundly influenced the Australian research community in terms of research collaboration, training and quality, and continues to play a leading role in promoting excellence in research training.

Research topics at UQ include management of complex and interconnected data including spatiotemporal, multimedia, social, scientific and environmental data, new computing architectures for real-time analytics in data-intensive applications, and effective use and governance of information systems.

Research in Data Science and Information Systems predominantly occurs in the School of Information Technology and Electrical Engineering and the UQ Business School, with significant contributions from other schools in the Faculty of Engineering, Architecture and Information Technology, and Faculty of Science.

There are also significant contributions in applied research made by the Queensland Brain Institute (QBI), the Institute for Molecular Bioscience (IMB) and the Institute for Social Science Research (ISSR).

Data Science and Information Systems research is supported by a high performance computing lab that provides the capacity to store, manage and analyse over 40TB of data using cluster, multi-core and in-memory computing architectures, as well as advanced data capture and visualisation capabilities.

UQ has particular expertise in the areas of:

Data and Knowledge Engineering

Information Governance, Modelling, Use and Quality

Application-centric Research in Data-Intensive Domains

Information Systems and Data Management in brief

More than 75 full-time equivalent researchers

More than 120 PhD and MPhil students in 2014

More than 1150 publications since 2008

More than $24.5 million in research funding since 2008

Information Systems research rated at the highest level – well above world standard – in the 2012 Excellence in Research for Australia exercise

Highlights of UQ Information Systems and Data Management

Developing effective and efficient solutions for managing, integrating and analysing large amounts of complex and heterogeneous data

The emergence of large, diverse, and publicly available data sets has led to the phenomenon of ‘Big Data’, which brings us closer than ever before to the promise of data-driven decision-making. However, diversity, scale and complexity present very real challenges in generating valuable information from big data. Researchers in UQ’s data and knowledge engineering group are at the international forefront – developing effective and efficient solutions for managing, integrating and analysing large amounts of complex and heterogeneous data.

UQ researchers have specifically targeted spatiotemporal and multimedia data, areas that have seen massive growth in volume and use due to prevalence of GPS devices and multimedia-enabled smartphones. Led by renowned Professors Xiaofang Zhou and Heng Tao Shen, this group has developed novel multi-resolution map data access methods, data mining approaches, trajectory search algorithms, a highly efficient near-duplicate video retrieval system capable of detecting duplicates among millions of video clips, and novel approaches to real-time content-based multimedia search. The research has been immensely successful in attracting funding and interest from major corporate players in the software industry such as Microsoft and SAP.

Information governance, modelling, use and quality to develop a holistic view of the information chain

The interconnectedness and diversity of data within the analytics pipeline challenges user trust of the reliability and interpretability of the results. An understanding of the underlying quality of data, the supporting data governance and audit frameworks, and the factors that lead to the effective use of information systems, are imperative to develop a holistic view of the information chain. UQ researchers conduct studies that provide deep insights into the impact of information governance, modelling, use and quality.

UQ has a strong history of influential work on conceptual modelling and continues to make a significant impact on theory and practice in this area. This research includes highly cited works by Professor Andrew Burton-Jones on theorising how representation is at the heart of effective use of information systems, and how it informs the design of information systems that can be more effectively deployed by users. This investigation includes for example, studying effective use of electronic health records with health authorities, and has attracted funding from the ARC, including an ARC Future Fellowship. Professor Ron Weber's highly cited research on conceptual modelling includes theorising about factors that lead to high quality conceptual models. His work has investigated the effect of conceptual models of varying qualities on cognitive engagement and continues to investigate the theoretical underpinnings of conceptual modelling grammars. UQ researchers, Professors Shazia Sadiq and Marta Indulska focus on the governance and quality of data and lead a number of initiatives for improved cross-fertilisation between research, industry and user groups in the area of data quality management, which include formation of a community of practice in data quality in in the Asia-Pacific region, as well as establishing a data quality roundtable in Queensland.

Application-centric research in data-intensive domains to investigate innovative approaches to management, analysis and visualisation services for large-scale data collections

Recognising the critical importance of application drivers in defining and pursuing meaningful problems, UQ researchers consistently position their research across a broad range of applications spanning business, scientific and social domains.

The eResearch group led by Professor Jane Hunter is involved in a large number of data-centric applications in both the sciences and humanities. The group investigates innovative approaches to management, analysis and visualisation services for large-scale data collections to accelerate scientific discovery. The common aim is to expedite research outcomes through the sharing, integration and analysis of open access data, using Semantic Web and Linked Open Data approaches. Hunter’s group has attracted funding from Microsoft Research, the Mellon Foundation and the ARC to develop ontology-based data integration and reasoning services for numerous agencies including the Great Barrier Reef Foundation, the Australian Bureau of Statistics, and the Atlas of Living Australia.

The research results of the data and knowledge engineering, eResearch, and business information systems groups have been applied to a large variety of data sets including GPS and trajectory data, sensor and news feeds, multimedia data, social data, bibliometric data, large scale machine/log data, and business data. The research has profound implications for new applications such as environmental resources management, intelligent transport systems, fleet management, location-based marketing and social networks, learning analytics, data-intensive science such as astronomy and genomics, public safety, emergency response and cyber-security, all of which can benefit from effective and efficient analysis of large amounts of diverse data.